5-2 Random Variables
... either a finite number of values or countable number of values, where “countable” refers to the fact that there might be infinitely many values, but they result from a counting process Continuous random variable infinitely many values, and those values can be associated with measurements on a cont ...
... either a finite number of values or countable number of values, where “countable” refers to the fact that there might be infinitely many values, but they result from a counting process Continuous random variable infinitely many values, and those values can be associated with measurements on a cont ...
Theorem 4.4. Let E and F` be two events. Then In words, the
... of this proof by formulating each step in the "numbers on flags" Ianguage. Before illustrating how Theorem 4.4 is used in a particular example, we deduce two more results. Theorem 4.5. If E and F are mutually exclusioe events, then ...
... of this proof by formulating each step in the "numbers on flags" Ianguage. Before illustrating how Theorem 4.4 is used in a particular example, we deduce two more results. Theorem 4.5. If E and F are mutually exclusioe events, then ...
ECE 302 Spring 2012 Ilya Pollak
... the following inequality holds: E[L|L > n] > n Let m be a number such that P(L ≤ m) > 0. Show that the following inequality holds: E[L|L ≤ m] ≤ m Solution. Consider the random variable X = L − n. The event L > n is then equivalent to the event X > 0. Given this event, all nonpositive outcomes for X ...
... the following inequality holds: E[L|L > n] > n Let m be a number such that P(L ≤ m) > 0. Show that the following inequality holds: E[L|L ≤ m] ≤ m Solution. Consider the random variable X = L − n. The event L > n is then equivalent to the event X > 0. Given this event, all nonpositive outcomes for X ...
Slides
... • The “prob. dense area” shrinks as dimension d arises • Harder to sample in this area to get enough information of the distribution • Acceptance rate decreases exponentially with d ...
... • The “prob. dense area” shrinks as dimension d arises • Harder to sample in this area to get enough information of the distribution • Acceptance rate decreases exponentially with d ...
